# Algebra for Machine Learning培训

algebraforml

## 课程时长

14 小时 通常来说是2天，包括中间休息。

## 要求

• Basic experience or familiarity with machine learning
• Basic programming experience

## 课程概览

• 理解基本的线性代数概念
• 学习机器学习所需的线性代数技能
• 在处理数据，图像，算法等时使用线性代数结构和概念。
• 使用线性代数解决机器学习问题

• 开发商
• 工程师

• 部分讲座，部分讨论，练习和繁重的实践练习

• 要申请本课程的定制培训，请联系我们安排。

Machine Translated

## 课程大纲

Introduction to Linear Algebra

Why You Should Improve Your Linear Algebra Knowledge for Machine Learning

Learning Linear Algebra Notations

Understanding Vectors

• Vector Properties and Characteristics
• Performing Vector Operations

Understanding Matrices

• Matrix Properties and Characteristics
• Performing Matrix Operations and Transformations
• Working with Special Matrices

Solving Linear Systems

• Representing Problems as Linear Systems
• Solving Linear Systems

Linear Mappings with Matrices

• Orthogonal Matrices
• The Gram-Schmidt Process

Reflecting and Manipulating Images with Matrices

Understanding Eigenvalues and Eigenvectors and their Application to Data Problems

Examining Google's PageRank Algorithm with Eigenvalues and Eigenvectors

Understanding Principal Components Analysis (PCA) for Machine Learning

Understanding Linear Regression for Machine Learning

Project: Solving a Machine Learning Problem with Linear Algebra

Summary and Conclusion

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